AI Threat Alert
CVE-2025-12343
MEDIUMCVE-2025-12343 is a medium-severity DoS vulnerability in FFmpeg's TensorFlow DNN backend affecting AI/ML media processing pipelines. The local attack vector and required user interaction significantly limit real-world exploitability, but automated media ingestion pipelines effectively eliminate the 'user interaction' barrier. Patch FFmpeg in any environment using TensorFlow-based DNN inference for video/audio processing, and validate that untrusted media files cannot reach FFmpeg's DNN backend directly.
Affected Systems
| Package | Ecosystem | Vulnerable Range | Patched |
|---|---|---|---|
| ffmpeg | — | — | No patch |
Do you use ffmpeg? You're affected.
Severity & Risk
Recommended Action
- 1. Inventory FFmpeg versions across AI/ML infrastructure, particularly in media preprocessing and computer vision pipelines. 2. Apply available vendor patches from RedHat (CVE-2025-12343 advisory); monitor upstream FFmpeg for official patch version. 3. If patching is not immediately possible, disable FFmpeg's TensorFlow DNN backend (dnn_backend_tf) in non-essential workloads. 4. Implement input validation and sandboxing for media files processed through FFmpeg — run FFmpeg in isolated containers with resource limits to contain crash impact. 5. Add crash monitoring and alerting on FFmpeg processes in AI/ML pipelines (unexpected exits, OOM signals). 6. In automated pipelines, enforce file-type and content validation upstream before DNN processing.
Classification
Compliance Impact
This CVE is relevant to:
Technical Details
NVD Description
A flaw was found in FFmpeg’s TensorFlow backend within the libavfilter/dnn_backend_tf.c source file. The issue occurs in the dnn_execute_model_tf() function, where a task object is freed multiple times in certain error-handling paths. This redundant memory deallocation can lead to a double-free condition, potentially causing FFmpeg or any application using it to crash when processing TensorFlow-based DNN models. This results in a denial-of-service scenario but does not allow arbitrary code execution under normal conditions.
Exploitation Scenario
An adversary targeting an AI-powered media analysis service (e.g., automated video moderation, content classification) uploads a specially crafted media file designed to trigger error-handling paths in FFmpeg's TensorFlow DNN backend. When the pipeline calls dnn_execute_model_tf() to run inference on the file, the double-free is triggered, crashing the FFmpeg worker process. In a poorly isolated architecture, this crash propagates to the inference service, causing repeated DoS against the AI pipeline. An adversary can automate this by bulk-uploading malicious files, causing sustained service disruption with minimal effort and no elevated privileges.
Weaknesses (CWE)
CVSS Vector
CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:N/I:N/A:H References
- access.redhat.com/security/cve/CVE-2025-12343 3rd Party
- bugzilla.redhat.com/show_bug.cgi Issue 3rd Party